setBinaryTargetLevel

For binary classification problems, ensemble
stacks and certain performance measures require an awareness
of which class in a two-factor outcome is the "target" class.
By default, the first level in an outcome factor is used but
this value can be overridden using <code>setBinaryTargetLevel(2L)</code>

Functions for creating ensembles of caret models: caretList()
and caretStack().  caretList() is a convenience function for fitting multiple
caret::train() models to the same dataset. caretStack() will make linear or
non-linear combinations of these models, using a caret::train() model as a
meta-model, and caretEnsemble() will make a robust linear combination of
models using a GLM.

Zachary A Mayer

caretEnsemble

Ensembles of Caret Models

setBinaryTargetLevel function

<dl><dt>level</dt>
<dd>an integer in {1, 2} to be used as target outcome level</dd></dl>

Arguments

Set the target binary class level — setBinaryTargetLevel

<dl>

<dt>level</dt>
<dd>an integer in {1, 2} to be used as target outcome level</dd>

</dl>

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setBinaryTargetLevel: Set the target binary class level

Description

Usage

Arguments

See Also